Quartz is the most abundant mineral on the earth’s surface. It is spectrally active in the longwave infrared (LWIR) region with no significant spectral features in the optical domain, i.e., visible–near-infrared–shortwave-infrared (Vis–NIR–SWIR) region. Several space agencies are planning to mount optical image spectrometers in space, with one of their missions being to map raw materials. However, these sensors are active across the optical region, making the spectral identification of quartz mineral problematic. This study demonstrates that indirect relationships between the optical and LWIR regions (where quartz is spectrally dominant) can be used to assess quartz content spectrally using solely the optical region. To achieve this, we made use of the legacy Israeli soil spectral library, which characterizes arid and semiarid soils through comprehensive chemical and mineral analyses along with spectral measurements across the Vis–NIR–SWIR region (reflectance) and LWIR region (emissivity). Recently, a Soil Quartz Clay Mineral Index (SQCMI) was developed using mineral-related emissivity features to determine the content of quartz, relative to clay minerals, in the soil. The SQCMI was highly and significantly correlated with the Vis–NIR–SWIR spectral region (R²= 0.82, root mean square error (RMSE) = 0.01, ratio of performance to deviation (RPD) = 2.34), whereas direct estimation of the quartz content using a gradient-boosting algorithm against the Vis–NIR–SWIR region provided poor results (R²= 0.45, RMSE = 15.63, RPD = 1.32). Moreover, estimation of the SQCMI value was even more accurate when only the 2000–2450 nm spectral range (atmospheric window) was used (R²= 0.9, RMSE = 0.005, RPD = 1.95). These results suggest that reflectance data across the 2000–2450 nm spectral region can be used to estimate quartz content, relative to clay minerals in the soil satisfactorily using hyperspectral remote sensing means.

石英是地球表面上最丰富的矿物。它在长波红外（LWIR）区域具有光谱活性，在光学域中没有明显的光谱特征，即可见-近红外-短波红外（Vis-NIR-SWIR）区域。几个太空机构正计划将光学图像光谱仪安装在太空中，其任务之一是绘制原材料图。但是，这些传感器在整个光学区域都处于活动状态，这使石英矿物的光谱识别成为问题。这项研究表明，光学和LWIR区域（石英在光谱上占主导地位）之间的间接关系可用于仅使用光学区域来光谱评估石英含量。为此，我们利用了以色列传统的土壤光谱库，它通过全面的化学和矿物分析以及Vis–NIR–SWIR区域（反射率）和LWIR区域（发射率）的光谱测量来表征干旱和半干旱土壤。最近，利用矿物相关的发射率特征开发了土壤石英粘土矿物指数（SQCMI），以确定土壤中相对于粘土矿物的石英含量。SQCMI与Vis–NIR–SWIR光谱区域（R² = 0.82，均方根误差（RMSE）= 0.01，性能与偏差之比（RPD）= 2.34），而对Vis–NIR–SWIR区域使用梯度增强算法直接估算石英含量可得到较差的结果（R ² = 0.45，RMSE = 15.63，RPD = 1.32）。此外，仅使用2000–2450 nm光谱范围（大气窗口）时，SQCMI值的估计甚至更加准确（R ² = 0.9，RMSE = 0.005，RPD = 1.95）。这些结果表明，使用高光谱遥感手段，可以将2000-2450 nm光谱范围内的反射率数据用于相对于土壤中粘土矿物的石英含量进行估算。